Dynamically updating digital visual content via aggregated feedback

ABSTRACT

Embodiments can include receiving, by a data processing system, digital teaching content monitoring, by the data processing system, user gaze of the digital teaching content.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/249,016, filed Jan. 16, 2019, entitled, “Dynamically Updating DigitalVisual Content Via Aggregated Feedback” which is incorporated herein byreference in its entirety, which is incorporated herein by reference inits entirety.

BACKGROUND

Lecture presentations through text and image content, such as slidescontaining text and/or images, is a widespread teaching method inschools and colleges. Lectures are replicated over days, and semesters,and are the most common form of mass education. However, the classroompresentation material is seldom revised, due to the lack of time andmotivation on the part of a busy lecturer. These revisions, as and whenthey happen, may not capture class attention dynamics, but only end ofsemester feedback or average assignment scores. Further, with largeclassrooms and short class times, a class session might not be able tofully connect or connect at all to the content in the reading material(books, MOOC video, animation).

SUMMARY

Shortcomings of the prior art are overcome and additional advantages areprovided through the provision, in one aspect, of a computer-implementedmethod of ordering digital teaching content. The method includes:receiving, by a data processing system, digital teaching content and anycorresponding annotations and one or more content heuristic from ateacher, wherein the digital teaching content comprises at least one oftext and one or more image; monitoring, by the data processing system,student gaze of the digital teaching content during a class by aplurality of students; and cognitively skipping, by the data processingsystem, some of the digital teaching content during the class based onthe monitoring and the one or more content heuristic.

In another aspect, a system for ordering digital teaching content may beprovided. The system may include, for example, memory, at least oneprocessor in communication with the memory, the memory storing programinstructions executable by the at least one processor to perform amethod. The method may include, for example: receiving, by a dataprocessing system, digital teaching content and any correspondingannotations and one or more content heuristic from a teacher, whereinthe digital teaching content comprises at least one of text and one ormore image; monitoring, by the data processing system, student gaze ofthe digital teaching content during a class by a plurality of students;and cognitively skipping, by the data processing system, some of thedigital teaching content during the class based on the monitoring andthe one or more content heuristic.

In a further aspect, a computer program product may be provided. Thecomputer program product may include a storage medium readable by aprocessor and storing instructions for performing a method. The methodmay include, for example: receiving, by a data processing system,digital teaching content and any corresponding annotations and one ormore content heuristic from a teacher, wherein the digital teachingcontent comprises at least one of text and one or more image;monitoring, by the data processing system, student gaze of the digitalteaching content during a class by a plurality of students; andcognitively skipping, by the data processing system, some of the digitalteaching content during the class based on the monitoring and the one ormore content heuristic.

Further, services relating to one or more aspects are also described andmay be claimed herein.

Digital teaching content ordering includes receiving, by a dataprocessing system, digital teaching content and any correspondingannotations and content heuristic(s) from a teacher or other presenter,the digital teaching content including text and/or image(s). The dataprocessing system monitors student gaze of the digital teaching contentduring a class and can cognitively skip some of the digital teachingcontent during the class based on the monitored student gaze and contentheuristic(s). The system can also cognitively modify the digitalteaching content outside of class based, in part, on the monitoring andthe content heuristic(s). A hypergraph of the digital teaching contentmay also be received, the cognitively skipping being further based onthe hypergraph, and the cognitively modifying being further based on aheat map built using the hypergraph and the student gaze.

Additional features are realized through the techniques set forthherein. Other embodiments and aspects, including but not limited tomethods, computer program product and system, are described in detailherein and are considered a part of the claimed invention.

BRIEF DESCRIPTION OF THE DRAWINGS

One or more aspects are particularly pointed out and distinctly claimedas examples in the claims at the conclusion of the specification. Theforegoing and objects, features, and advantages of one or more aspectsare apparent from the following detailed description taken inconjunction with the accompanying drawings in which:

FIG. 1 is a flow diagram for one example of a computer-implementedmethod of ordering digital teaching content, in accordance with one ormore aspects of the present disclosure.

FIG. 2 is a flow diagram for automatically cognitively modifying digitalteaching content during class, in accordance with one or more aspects ofthe present disclosure.

FIG. 3 is a flow diagram for a more detailed example of using heat mapsto order electronic teaching content, in accordance with one or moreaspects of the present disclosure.

FIG. 4 depicts one example of a classroom during a teaching session, inaccordance with one or more aspects of the present disclosure.

FIG. 5 is a modified block diagram of one example of a systemarchitecture for automatically performing the cognitive functionsdescribed herein, in accordance with one or more aspects of the presentdisclosure.

FIG. 6 depicts one example of an initial hypergraph of digital teachingcontent, in accordance with one or more aspects of the presentdisclosure.

FIG. 7 depicts one example of a final ordering of the digital teachingcontent depicted in the hypergraph of FIG. 6 , in accordance with one ormore aspects of the present disclosure.

FIG. 8 depicts another example of an initial hypergraph of digitalteaching content, in accordance with one or more aspects of the presentdisclosure.

FIG. 9 depicts one example of a final ordering of the digital teachingcontent depicted in the hypergraph of FIG. 8 , in accordance with one ormore aspects of the present disclosure.

FIG. 10 depicts another example of a classroom teaching session with thedigital teaching content being partitioned into zones, in accordancewith one or more aspects of the present disclosure.

FIG. 11 is a block diagram of one example of a computer system, inaccordance with one or more aspects of the present disclosure.

FIG. 12 is a block diagram of one example of a cloud computingenvironment, in accordance with one or more aspects of the presentdisclosure.

FIG. 13 is a block diagram of one example of functional abstractionlayers of the cloud computing environment of FIG. 12 , in accordancewith one or more aspects of the present disclosure.

DETAILED DESCRIPTION

One or more aspect of this disclosure relate, in general, to digitalvisual content used, for example, in a class setting. More particularly,one or more aspect of the disclosure relate to automatically updatingdigital visual content used in, for example, a class setting based onaggregate student gaze of the digital material.

This disclosure addresses the noted problem, by using gaze of digitalteaching content (more generally, “digital visual content”) presentedvisually to all students in a class session. Gaze is used as a measureof classroom attention to the digital visual content. This is then usedto cognitively reason how much content is to be provided for theparticular topic, content is redesigned, skipped if needed, and/orreordered to reflect a more easily consumable version of the contentusing a hypergraph data structure.

A hypergraph of the digital teaching content and heuristics for thecontent are received from, for example, a teacher or an intermediary forthe teacher. Next, a sequence of gaze points from a class session isgathered. The system then builds heat map(s) at a class level that isused to order the digital teaching content (e.g., material presented onslides) based, in one embodiment, on descending order of attention. Theheuristics are then used to reason what level of content clarity isrequired, content is skipped if needed during the session, and/orrearranged after the class session.

As used herein, the term “fixation” refers to when the eye gaze pausesin a certain position, say while reading a text, viewing an image etc.For example, human eyes tend to fixate on a few words of a givensentence, as they read the sentence.

As used herein, the term “saccade” refers to when the eye gaze movesfrom one position to another (the movement is the saccade).

As used herein, the term “scanpath” refers to a sequence of fixationsand saccades.

As used herein, the term “heat map” when used with respect to gaze,refers to an aggregate visual representation of the visual explorationpatterns in a group of users. In these representations, the hot zones orzones with higher density designate where the users focused their gaze(not their attention) with a higher frequency and/or a higher totalduration.

As used herein, the term “hypergraph” refers to a graph wherein eachhypernode is a group of alternative related concepts (none of these is abackground/dependent concept of another—one can replace another withoutbreaking the remaining concept set present in the material), and eachhyperedge connects a pair of hypernodes.

For example, in FIG. 6 , there are three hypernodes—the first one hasthree concept groups, while the second one has two concept groups andthe third hypernode is a singleton “normal” graph node. This isdiscussed in more detail below.

The following explains one method to summarize class gaze.

Fixation is determined from the intersection of the gaze vector and thedigital teaching content. The most widely used current technologies arevideo-based eye-trackers. A camera focuses on one or both eyes andrecords eye movement as the viewer looks at some kind of stimulus, inthis case, digital teaching content. Most modern eye-trackers use thecenter of the pupil and infrared or near-infrared non-collimated lightto create corneal reflections. The vector between the pupil center andthe corneal reflections can be used to determine the point of regard ona surface or the gaze direction. A simple calibration procedure of theindividual may be performed before using the eye tracker.

Two general types of infrared/near-infrared (also known as active light)eye-tracking techniques are common: bright-pupil and dark-pupil. Theirdifference is based on the location of the illumination source withrespect to the optics. If the illumination is coaxial with the opticalpath, then the eye acts as a retroreflector as the light reflects offthe retina creating a bright pupil effect similar to red eye. If theillumination source is offset from the optical path, then the pupilappears dark because the retroreflection from the retina is directedaway from the camera.

Bright-pupil tracking creates greater iris/pupil contrast, allowing morerobust eye-tracking with all iris pigmentation, and greatly reducesinterference caused by eyelashes and other obscuring features. It alsoallows tracking in lighting conditions ranging from total darkness tovery bright.

Another, less used, method is known as passive light. It uses visiblelight to illuminate, something which may cause some distractions tousers. Another challenge with this method is that the contrast of thepupil is less than in the active light methods, therefore, the center ofiris is used for calculating the vector instead. This calculation needsto detect the boundary of the iris and the white sclera (limbustracking). It presents another challenge for vertical eye movements dueto obstruction of eyelids.

Eye-tracking setups vary greatly: some are head-mounted, some requirethe head to be stable (for example, with a chin rest), and some functionremotely and automatically track the head during motion. Most use asampling rate of at least 30 Hz. Although 50/60 Hz is more common, todaymany video-based eye trackers run at 240, 350 or even 1000/1250 Hz,speeds needed in order to capture fixational eye movements or correctlymeasure saccade dynamics.

Eye movements are typically divided into fixations and saccades—when theeye gaze pauses in a certain position, and when it moves to anotherposition, respectively. The resulting series of fixations and saccadesis called a scanpath. Smooth pursuit describes the eye following amoving object. Fixational eye movements include microsaccades: small,involuntary saccades that occur during attempted fixation. Mostinformation from the eye is made available during a fixation or smoothpursuit, but not during a saccade.

Scanpaths are useful for analyzing cognitive intent, interest, andsalience. Other biological factors may affect the scanpath as well. Eyetracking in human—computer interaction (HCl) typically investigates thescanpath for usability purposes, or as a method of input ingaze-contingent displays, also known as gaze-based interfaces.

Eye-trackers necessarily measure the rotation of the eye with respect tosome frame of reference. This is usually tied to the measuring system.Thus, if the measuring system is head-mounted, as with EOG or avideo-based system mounted to a helmet, then eye-in-head angles aremeasured. To deduce the line of sight in world coordinates, the headmust be kept in a constant position or its movements must be tracked aswell. In these cases, head direction is added to eye-in-head directionto determine gaze direction.

If the measuring system is table-mounted, as with scleral search coilsor table-mounted camera (“remote”) systems, then gaze angles aremeasured directly in world coordinates. Typically, in these situationshead movements are prohibited. For example, the head position is fixedusing a bite bar or a forehead support. Then a head-centered referenceframe is identical to a world-centered reference frame. Or colloquially,the eye-in-head position directly determines the gaze direction.

Some results are available on human eye movements under naturalconditions where head movements are allowed as well. The relativeposition of eye and head, even with constant gaze direction, influencesneuronal activity in higher visual areas.

For all gaze vectors, determine the following fixation features, thatcan potentially be stored as a part of the third entry (fixationfeatures entry) of the above triplet (i.e., in FIG. 6 ): total fixationduration (TFD); number of fixation durations (NF); average fixationdurations (AFD); total time tracked per slide (TTT); percentage ofviewing time (PVT); and total fixation duration on content zones (TFP).TFD is the total duration of eye gaze fixation to a given material, NFis the number of times the eye gaze fixated, AFD=TFD/NF, TTT is simplythe total time the system could track the viewing behavior of studentstowards a given slide, PVT is the percentage of time the students at allviewed the screen and TFP is the total time duration of eye gazefixation towards a zone of content.

Using these, a percentage of time spent in the content zone(PTZ)=TFP/TVS, where TVS is the total visual saliency. That is, if acontent page/slide has, for example, three visually salient zones(content zones that the students would look at), and if the totalfixation duration on the page/slide is 120 seconds, then PTZ=120/3=40.

A cluster average of saccade paths (ASP)=nearest vectors of saccadepaths averaged to find set of average saccade paths.

In one example, the digital teaching content may include metadata.Adding metadata involves tagging content type (e.g., text, image), atopic of each content region unit. For example, a content region unitmay include: one slide of a presentation application, a concept areamarked by a teacher, or an automatically derived concept zone usingexisting Natural Language Understanding (NLU) techniques. Addingmetadata may further include a minimum understanding required (notion ofimportance, i), minimum attention required (notion of effort, e), minand max time of show for content region unit.

Similar to the hypergraph, for purposes of the present application, howthe heuristic(s) are generated is not relevant, only that theheuristic(s) is provided. In one example, the teacher can defineheuristics for preferring a particular type or source of content.

Define a min and max total time of lecture to be put as a constraint.Next, a sequence of gaze points from a class session is gathered using,for example, a single monocular camera. During a class, summarystatistics and a heat map are generated at the class level to assign anattention score to each element in each content region for all thecontent regions (notion of attention, a).

Then the system builds a class-level heat map that is used to rate thecontent based in descending order of attention. In one example, theratings may subsequently be used in ordering the digital teachingcontent. The generated heat map may also be used for other purposes.

For each content region unit, maintain a tuple, <content-id, iimportance, and effort e>. Compute an attention score for nearestcontent, based on a maximum area overlap. Here, “maximum area overlapcan be thought of as a Venn diagram kind of overlap—which area receiveseye gaze, and which content zone is physically overlapping the most withthis area. Attention score(a)=f (average time on content as a percentageof total time on the content region unit, physical area of renderedcontent, cluster average of saccadepaths (ASP)). This refers to howattentive a student was, to a given region where a given content ofgiven type was shown.

Next, construct <content-id, i, e, a> for each content region unit andcompute average attention.

The heuristic(s) are then used to reason what level of content clarityis required. In one example, low attention observed over some of thedigital teaching content that has high importance and high attentionrequirement, allows the system to reason low understanding for thatcontent, thus new content with greater clarity is required. For eachcontent region unit in each hypernode, factor the average f(i, e) by a,such that high attention, high importance, low effort of the contentregion units are given the lowest edge weights. Thus, f^(new)_(slide)=Ω(f_(slide) (i. e.), a_(slide)). Next, find set of contentregion units that minimize the shortest f and least total time of slides(T). Accordingly, the newer version of the slide content is a functionof the older version of the slide content, scaled with the attention atits different parts/zones.

As the attention scores are being generated, in real time: calculate theT and shortest f scores iteratively. Here f is function that determinesthe change in slide content when moving from the older to the newerslide. Calculate whether the shortest f or T scores change beyond apredefined threshold (e.g., defined by the teacher); drop the nextcontent region units in the node or send an alert to the teacher so thatthe f and T are maintained. Retain the content regions with favorable f,T scores or a combination of f and T at each hypernode, and drop theremaining concept groups in each hypernode except the one retainedconcept. The retained path becomes the dynamically generated learningpathway that factors for the student group attention and understandingin the class.

As used herein, the term “digital teaching content” refers to any andall digital content presented visually by a teacher to a class for thepurpose of teaching the class. Digital teaching content includes, forexample, text and/or image(s). The term also includes any annotations orother entries made by the teacher to the digital teaching content. Inone example, the digital teaching content is in the form of slides froma presentation program. More broadly, the term “digital visual content”can be applied similarly, but not necessarily teaching in a classroomsetting. For example, it could be used at financial, marketing or otherbusiness settings in which the digital visual content is used and thepresentation is repeated at least twice.

As used herein, the term “gaze” when used with regard to students orattendees in a class or lecture or presentation, refers to wherestudent(s) eyes are looking at the digital teaching content beingpresented visually.

As used herein, the term “cognitively skipping” refers to the use ofcognitive computing to determine, during a class, whether some digitalteaching content should be skipped and skipping it, the determiningbased on the content heuristic(s) provided by the teacher and in-classmonitoring of student gaze with respect to the digital teaching content.

As used herein, the term “cognitively modifying” refers to the use ofcognitive computing to automatically modify digital teaching contentoutside of class based on a hypergraph of student gaze with regard tothe digital teaching content during a class session and contentheuristic(s) provided by the teacher. The modifying can include addingnew digital teaching content, revising one or more portion of thedigital teaching content, and removing some of the digital teachingcontent.

As used herein, the term “heuristics” when used with digital teachingcontent refers generally to a “flow” of the digital teaching content,and more specifically to dependencies between content regions of thedigital teaching content, which may be provided by the teacher. Forexample, it may be that one content region defines terms, while anothercontent region includes an example in which the terms are used. In oneembodiment, the teacher provides the heuristics to the system.

A heat map (or heatmap) is a graphical representation of data where theindividual values contained in a matrix are represented as colors or ingray scale.

Web heat maps are used for displaying areas of a Web page mostfrequently scanned by visitors. For example, web heat maps are oftenused alongside other forms of web analytics and session replay tools.

In mathematics, a hypergraph is a generalization of a graph in which anedge can join any number of vertices. Formally, a hypergraph H is a pairH=(X, E) where X is a set of elements called nodes or vertices, and E isa set of non-empty subsets of X called hyperedges or edges. Therefore, Eis a subset of P(X)\{Ø}, where P(X)} is the power set of X.

While graph edges are pairs of nodes, hyperedges are arbitrary sets ofnodes, and can therefore contain an arbitrary number of nodes. However,it is often desirable to study hypergraphs where all hyperedges have thesame cardinality; a k-uniform hypergraph is a hypergraph such that allits hyperedges have size k. (In other words, one such hypergraph is acollection of sets, each such set a hyperedge connecting k nodes.) So a2-uniform hypergraph is a graph, a 3-uniform hypergraph is a collectionof unordered triples, and so on.

A hypergraph is also called a set system or a family of sets drawn fromthe universal set X. The difference between a set system and ahypergraph is in the questions being asked. Hypergraph theory tends toconcern questions similar to those of graph theory, such as connectivityand colorability, while the theory of set systems tends to asknon-graph-theoretical questions.

There are variant definitions; sometimes edges must not be empty, andsometimes multiple edges, with the same set of nodes, are allowed.

FIG. 1 is a flow diagram 100 for one example of a computer-implementedmethod of ordering digital teaching content, in accordance with one ormore aspects of the present disclosure. The method begins with receiving102, by a data processing system, as described herein, digital teachingcontent and content heuristic(s) from a teacher, for example, over anetwork (e.g., the Internet). The digital teaching content includes, forexample, text and/or image(s). During a class session, the dataprocessing system monitors 104 student gaze of the digital teachingcontent. Based on student gaze, the data processing system cancognitively skip 106 material (with notice to the teacher) determinednot to be needed.

FIG. 2 is a flow diagram 200 for automatically cognitively modifyingdigital teaching content during class, in accordance with one or moreaspects of the present disclosure. FIG. 2 essentially replacescognitively skipping 106 in FIG. 1 , though of course both can be done.The system receives 202 a hypergraph. More specifically, a hypergraph ofthe digital teaching content, and any annotations of the digitalteaching content for subsequently reordering the same, are received bythe data processing system, for example, from the teacher. For purposesof this disclosure, how the teacher prepares, or has prepared, thehypergraph is not relevant; only that one is provided. With thehypergraph, the system cognitively modifies 204 the digital teachingcontent cross-session, i.e., between sessions of the class.

FIG. 3 is a flow diagram 300 for a more detailed example of using heatmaps to order electronic teaching content, in accordance with one ormore aspects of the present disclosure. A sequence of gaze points aregathered 302, which may include one or more comment. Heat map(s) arebuilt 304 from the gaze points. Optionally, the digital teaching contentcan be divided 306 into zones that are reflected in the heat map(s). Ineither case, the heat map(s) are then used 308 to order the digitalteaching content.

FIG. 4 depicts one example of a classroom 400 during a teaching session,in accordance with one or more aspects of the present disclosure. Theclassroom, in this example, includes a lecturer 402 discussing a topicwith a class 404 of students with digital teaching content 406 beingvisually shown to the class via display 408. In one example, the digitalteaching content includes a group of slides from a presentationapplication. While the class is in session, a video camera 410 tracksstudent gaze on the digital teaching content.

FIG. 5 is a modified block diagram of one example of a systemarchitecture 500 for automatically performing the cognitive functionsdescribed herein, in accordance with one or more aspects of the presentdisclosure. Eye gaze tracking module 502 tracks the gaze of students 504on digital teaching content 506. A gaze metric computation module 508calculates the gaze (e.g., average gaze) as described above. Anattention scoring module 510 scores attention of the students on thedigital teaching content. In one example using presentation slides, eachslide could be logically partitioned into zones and the attentionscoring module can provide scores for all the zones. A hypergraph 512 ofthe digital teaching content and heuristics 514 are used by a contentarrangement optimize and skip module 516 to cognitively determine anoptimum arrangement of the digital teaching content and determiningwhat, if any, of the digital teaching content could be skipped. Where itis determined, for example, that the student gaze for particularportions of the digital teaching content is too low, a content generator518 may be used to automatically generate digital teaching content, forexample, adding additional details to the digital teaching content orreplacing some of the digital teaching content. In that regard NaturalLanguage Understanding (NLU), discussed in more detail below, can beused to process the digital teaching content for understanding itsintended meaning. In addition, the digital teaching content and NLUoutput could be stored (e.g., in a database) for machine learning.

FIG. 6 depicts one example of an initial hypergraph 600 of digitalteaching content, in accordance with one or more aspects of the presentdisclosure. The hypergraph includes three hypernodes 602, 604 and 606.Hypernodes 602 and 604 each have two or more concept groups, which arefungible. The first hypernode includes three concept groups 608, 610 and612. Concept group 610 includes two slides 609, while concept group 612includes three slides 611. Slides 609 are different from slides 611.Slides 609 have the lowest f_(slide) score of the three concept groups.Similarly, in hypernode 604, slides 609 have a lower f_(slide) scorethan slides 611.

FIG. 7 depicts one example of a final ordering of the digital teachingcontent depicted in the hypergraph of FIG. 6 , in accordance with one ormore aspects of the present disclosure. The final arrangement includesconcept groups 610, 616 and 606.

FIG. 8 depicts another example of an initial hypergraph 800 of digitalteaching content, in accordance with one or more aspects of the presentdisclosure. The hypergraph includes three hypernodes 802, 804 and 806.Hypernodes 802 and 804 each have two or more concept groups, which arefungible. The first hypernode 802 includes three concept groups 808, 810and 812. Concept group 810 includes two slides 611 and 609, whileconcept group 812 includes three slides 611. Slide 609 is different fromslides 611. Slide 609 in concept group 808 (also in 810) has the lowestf_(slide) score of the three concept groups. Similarly, in hypernode804, slides 609 have a lower f_(slide) score than slides 611.

FIG. 9 depicts one example of a final ordering of the digital teachingcontent depicted in the hypergraph of FIG. 8 , in accordance with one ormore aspects of the present disclosure. The final arrangement includesconcept groups 808, 816 and 806.

FIG. 10 is a modified block diagram for one example of a classroomteaching session 1000, in in accordance with one or more aspects of thepresent disclosure. A display 1002 of digital teaching content, thecontent including, for example, text (e.g., text 1004) and image(s)(e.g., image 1006). The display is divided into at least two zones orcontent regions, for example, content regions 1008, 1010, 1012 and 1014.Although four content regions are shown, it will be understood thatthere could be more or less content regions. In one embodiment, theteacher defines the content regions as part of the initial informationprovided to the system, i.e., the digital teaching content onheuristic(s). In another embodiment, the system defines one or morecontent region, all defined by the system or a combination of teacherdefined and system defined. In one example, the system can use imagerecognition to define an image as a content region. In another example,the system can use Natural Language Understanding (described more fullybelow) to identify and define text as a content region. A gaze detectionapparatus 1016, for example, a camera (e.g., a monocular camera) orother imaging apparatus, detects gaze points 1017 during a class fromthe eyes 1018 of students as they view 1020 content region(s) in thedisplayed digital teaching content.

Approximating language that may be used herein throughout thespecification and claims, may be applied to modify any quantitativerepresentation that could permissibly vary without resulting in a changein the basic function to which it is related. Accordingly, a valuemodified by a term or terms, such as “about,” is not limited to theprecise value specified. In some instances, the approximating languagemay correspond to the precision of an instrument for measuring thevalue.

As used herein, the terms “may” and “may be” indicate a possibility ofan occurrence within a set of circumstances; a possession of a specifiedproperty, characteristic or function; and/or qualify another verb byexpressing one or more of an ability, capability, or possibilityassociated with the qualified verb. Accordingly, usage of “may” and “maybe” indicates that a modified term is apparently appropriate, capable,or suitable for an indicated capacity, function, or usage, while takinginto account that in some circumstances the modified term may sometimesnot be appropriate, capable or suitable. For example, in somecircumstances, an event or capacity can be expected, while in othercircumstances the event or capacity cannot occur—this distinction iscaptured by the terms “may” and “may be.”

Spatially relative terms, such as “beneath,” “below,” “lower,” “above,”“upper,” and the like, may be used herein for ease of description todescribe one element's or feature's relationship to another element(s)or feature(s) as illustrated in the figures. It will be understood thatthe spatially relative terms are intended to encompass differentorientations of the device in use or operation, in addition to theorientation depicted in the figures. For example, if the device in thefigures is turned over, elements described as “below” or “beneath” otherelements or features would then be oriented “above” or “over” the otherelements or features. Thus, the example term “below” may encompass bothan orientation of above and below. The device may be otherwise oriented(e.g., rotated 90 degrees or at other orientations) and the spatiallyrelative descriptors used herein should be interpreted accordingly. Whenthe phrase “at least one of” is applied to a list, it is being appliedto the entire list, and not to the individual members of the list.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablestorage medium(s) having computer readable program code embodiedthereon.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

Certain embodiments herein may offer various technical computingadvantages involving computing advantages to address problems arising inthe realm of computer networks. Particularly, computer networksoperating to provide digital visual content presented by a presenter.Various decision data structures can be used to drive artificialintelligence (AI) decision making. Decision data structures as set forthherein can be updated by machine learning so that accuracy andreliability is iteratively improved over time without resource consumingrules intensive processing. Machine learning processes can be performedfor increased accuracy and for reduction of reliance on rules basedcriteria and thus reduced computational overhead. For enhancement ofcomputational accuracies, embodiments can feature computationalplatforms existing only in the realm of computer networks such asartificial intelligence platforms, and machine learning platforms.Embodiments herein can employ data structuring processes, e.g.processing for transforming unstructured data into a form optimized forcomputerized processing. Embodiments herein can cognitively skip some ofthe digital teaching content during the class based on student gaze andcontent heuristic(s). Embodiments herein can examine data from diversedata sources such as data sources that process radio signals forlocation determination of users. Embodiments herein can includeartificial intelligence processing platforms featuring improvedprocesses to transform unstructured data into structured form permittingcomputer based analytics and decision making. Embodiments herein caninclude particular arrangements for both collecting rich data into adata repository and additional particular arrangements for updating suchdata and for use of that data to drive artificial intelligence decisionmaking.

In one example, a cognitive computer system performs an analysis ofdigital visual content or participant facial and/or eye movements. Ingeneral, the term “cognitive computing” (CC) has been used to refer tonew hardware and/or software that mimics the functioning of the humanbrain and helps to improve human decision-making, which can be furtherimproved using machine learning. In this sense, CC is a new type ofcomputing with the goal of more accurate models of how the humanbrain/mind senses, reasons, and responds to stimulus. CC applicationslink data analysis and adaptive page displays (AUI) to adjust contentfor a particular type of audience. As such, CC hardware and applicationsstrive to be more effective and more influential by design.

Some common features that cognitive systems may express include, forexample: ADAPTIVE—they may learn as information changes, and as goalsand requirements evolve. They may resolve ambiguity and tolerateunpredictability. They may be engineered to feed on dynamic data in realtime, or near real time; INTERACTIVE—they may interact easily with usersso that those users can define their needs comfortably. They may alsointeract with other processors, devices, and Cloud services, as well aswith people; ITERATIVE AND STATEFUL—they may aid in defining a problemby asking questions or finding additional source input if a problemstatement is ambiguous or incomplete. They may “remember” previousinteractions in a process and return information that is suitable forthe specific application at that point in time; and CONTEXTUAL—they mayunderstand, identify, and extract contextual elements such as meaning,syntax, time, location, appropriate domain, regulations, user's profile,process, task and goal. They may draw on multiple sources ofinformation, including both structured and unstructured digitalinformation, as well as sensory inputs (e.g., visual, gestural, auditoryand/or sensor-provided).

Various decision data structures can be used to drive artificialintelligence (AI) decision making, such as decision data structure thatcognitively maps social media interactions in relation to posted contentin respect to parameters for use in better allocations that can includeallocations of digital rights. Decision data structures as set forthherein can be updated by machine learning so that accuracy andreliability is iteratively improved over time without resource consumingrules intensive processing. Machine learning processes can be performedfor increased accuracy and for reduction of reliance on rules basedcriteria and thus reduced computational overhead.

For enhancement of computational accuracies, embodiments can featurecomputational platforms existing only in the realm of computer networkssuch as artificial intelligence platforms, and machine learningplatforms. Embodiments herein can employ data structuring processes,e.g. processing for transforming unstructured data into a form optimizedfor computerized processing. Embodiments herein can examine data fromdiverse data sources such as data sources that process radio or othersignals for location determination of users. Embodiments herein caninclude artificial intelligence processing platforms featuring improvedprocesses to transform unstructured data into structured form permittingcomputer based analytics and decision making. Embodiments herein caninclude particular arrangements for both collecting rich data into adata repository and additional particular arrangements for updating suchdata and for use of that data to drive artificial intelligence decisionmaking.

As used herein, terms in the form of “cognitive <function>” refers tothe use of cognitive computing in performing the function. Cognitivecomputing is the simulation of human thinking, using software and/orhardware, which may be enhanced/improved using machine learning. Machinelearning is based in mathematics and statistical techniques, givingcomputer systems the ability to “learn” with data provided, e.g., arelatively large amount of data, without the need to be explicitlyprogrammed. The goal of cognitive computing is to create automatedsystems capable of solving problems without human assistance, broadlyreferred to as Artificial Intelligence (AI).

Artificial intelligence (AI) refers to intelligence exhibited bymachines. Artificial intelligence (AI) research includes search andmathematical optimization, neural networks and probability. Artificialintelligence (AI) solutions involve features derived from research in avariety of different science and technology disciplines ranging fromcomputer science, mathematics, psychology, linguistics, statistics, andneuroscience.

As used herein, the term “real-time” refers to a period of timenecessary for data processing and presentation to a user to take place,and which is fast enough that a user does not perceive any significantdelay. Thus, “real-time” is from the perspective of the user.

In one example, a machine learning process can update one or moreprocess run, based on obtained data to improve and accuracy and/orreliability of the one or more process. In one embodiment, for example,a decision data structure for various functions herein may be used.

In one embodiment, a number of instances of such a decision datastructure may be active, each instance for a different user. Such amachine learning process can continually or periodically update therelevant factors of the different instances of the decision datastructure.

An NLU process to process data for preparation of records that arestored in a data repository and for other purposes. In one example, aNatural Language Understanding (NLU) process can be used to process fordetermining one or more NLU output parameter of a text, for example.Such an NLU process can include one or more of a topic classificationprocess that determines topics of text and output one or more topic NLUoutput parameter. Running such an NLU process allows to perform a numberof processes.

In a first aspect, disclosed above is a computer-implemented method ofordering digital teaching content. The computer-implemented methodincludes: receiving, by a data processing system, digital teachingcontent and any corresponding annotations and content heuristic(s) froma teacher, the digital teaching content including at least one of textand image(s); monitoring, by the data processing system, student gaze ofthe digital teaching content during a class; and cognitively skipping,by the data processing system, some of the digital teaching contentduring the class based on the monitoring and the content heuristic(s).

In one example, the computer-implemented method may further include, forexample, cognitively modifying, by the data processing system, thedigital teaching content outside of class based, in part, on themonitoring and the content heuristic(s). In one example, the receivingmay further include, for example, receiving, by the data processingsystem, a hypergraph of the digital teaching content from the teacher,the cognitively skipping being further based on the hypergraph, and thecognitively modifying being further based on a heat map built using thehypergraph and the student gaze.

In one example, the cognitively modifying may include, for example, atleast one of generating new digital teaching content, removing some andless than all of the digital teaching content and reordering at leastsome of the digital teaching content. In one example, the cognitivelymodifying may include, for example, determining, by the data processingsystem, a level of clarity needed for content(s) of the digital teachingcontent using, at least in part, the content heuristic(s).

In one example, the computer-implemented method may further include, forexample, building, by the data processing system, heat map(s) from asequence of gaze points of the student gaze, a predetermined unit of thedigital teaching content being divided into zones, the monitoringincluding tracking gaze time of the students on each of the zones, andthe heat map(s) reflects the gaze time. In one example, thecomputer-implemented method may further include, for example, prior tothe cognitively skipping and the cognitively modifying, ordering, by thedata processing system, the digital teaching content in descending orderof attention based on the heat map(s).

In one example, the gathering may include, for example, using, by thedata processing system, camera(s) to observe student(s) in the class andobtain the sequence of gaze points.

In one example, the computer-implemented method of the first aspect mayfurther include, for example, using machine learning for the dataprocessing system to improve at least one of the cognitively skippingand the cognitively modifying.

In one example, the monitoring in the computer-implemented method of thefirst aspect may include, for example, gathering, by the data processingsystem, a sequence of gaze points, the method further includingbuilding, by the data processing system, heat map(s) from the sequenceof gaze points.

In a second aspect, disclosed above is a system for ordering digitalteaching content. The system includes: a memory; and processor(s) incommunication with the memory, the memory storing program code toperform a method. The method includes: receiving, by a data processingsystem, digital teaching content and any corresponding annotations andcontent heuristic(s) from a teacher, the digital teaching contentincluding at least one of text and image(s); monitoring, by the dataprocessing system, student gaze of the digital teaching content during aclass; and cognitively skipping, by the data processing system, some ofthe digital teaching content during the class based on the monitoringand the content heuristic(s).

In one example, the system may further include, for example, cognitivelymodifying, by the data processing system, the digital teaching contentoutside of class based, in part, on the monitoring and the contentheuristic(s), the receiving further includes receiving, by the dataprocessing system, a hypergraph of the digital teaching content from theteacher, the cognitively skipping being further based on a heat mapbuilt using the hypergraph and the student gaze, and the cognitivelymodifying being further based on the hypergraph. In one example, thesystem may further include, for example, building, by the dataprocessing system, heat map(s) from a sequence of gaze points of thestudent gaze, a predetermined unit of the digital teaching content isdivided into zones, the monitoring includes tracking gaze time of thestudents on each of the zones, and the heat map(s) reflects the gazetime.

In one example, the system of the second aspect may further include, forexample, cognitively modifying, by the data processing system, thedigital teaching content outside of class based, in part, on themonitoring and the content heuristic(s), the cognitively monitoringincludes gathering, by the data processing system, a sequence of gazepoints, the method further including building, by the data processingsystem, heat map(s) from the sequence of gaze points.

In one example, the system of the second aspect may further include, forexample: cognitively modifying, by the data processing system, thedigital teaching content outside of class based, in part, on themonitoring and the content heuristic(s); and machine learning for thedata processing system to improve at least one of the cognitivelyskipping and the cognitively modifying.

In a third aspect, disclosed above is a computer program product forordering digital teaching content, the computer-implemented methodincluding: a storage medium readable by a processor and storinginstructions for performing a method, the method including: receiving,by a data processing system, digital teaching content and anycorresponding annotations and content heuristic(s) from a teacher, thedigital teaching content including at least one of text and image(s);monitoring, by the data processing system, student gaze of the digitalteaching content during a class; and cognitively skipping, by the dataprocessing system, some of the digital teaching content during the classbased on the monitoring and the content heuristic(s).

In one example, the computer program product may further include, forexample, cognitively modifying, by the data processing system, thedigital teaching content outside of class based, in part, on themonitoring and the content heuristic(s), the receiving further includesreceiving, by the data processing system, a hypergraph of the digitalteaching content from the teacher, the cognitively skipping beingfurther based on a heat map built using the hypergraph and the studentgaze, and the cognitively modifying being further based on thehypergraph and the student gaze. In one example, the system may furtherinclude, for example, building, by the data processing system, heatmap(s) from a sequence of gaze points of the student gaze, apredetermined unit of the digital teaching content is divided intozones, the monitoring includes tracking gaze time of the students oneach of the zones, and the heat map(s) reflects the gaze time.

In one example, the computer program product of the third aspect mayfurther include, for example, cognitively modifying, by the dataprocessing system, the digital teaching content outside of class based,in part, on the monitoring and the content heuristic(s), the cognitivelymonitoring includes gathering, by the data processing system, a sequenceof gaze points, the method further including building, by the dataprocessing system, heat map(s) from the sequence of gaze points.

In one example, the computer program product of the third aspect mayfurther include, for example: cognitively modifying, by the dataprocessing system, the digital teaching content outside of class based,in part, on the monitoring and the content heuristic(s); and machinelearning for the data processing system to improve at least one of thecognitively skipping and the cognitively modifying.

FIGS. 11-13 depict various aspects of computing, including a computersystem and cloud computing, in accordance with one or more aspects setforth herein.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 11 , a schematic of an example of a computing nodeis shown. Computing node 10 is only one example of a computing nodesuitable for use as a cloud computing node and is not intended tosuggest any limitation as to the scope of use or functionality ofembodiments of the invention described herein. Regardless, computingnode 10 is capable of being implemented and/or performing any of thefunctionality set forth hereinabove. Computing node 10 can beimplemented as a cloud computing node in a cloud computing environment,or can be implemented as a computing node in a computing environmentother than a cloud computing environment.

In computing node 10 there is a computer system 12, which is operationalwith numerous other general purpose or special purpose computing systemenvironments or configurations. Examples of well-known computingsystems, environments, and/or configurations that may be suitable foruse with computer system 12 include, but are not limited to, personalcomputer systems, server computer systems, thin clients, thick clients,hand-held or laptop devices, multiprocessor systems,microprocessor-based systems, set top boxes, programmable consumerelectronics, network PCs, minicomputer systems, mainframe computersystems, and distributed cloud computing environments that include anyof the above systems or devices, and the like.

Computer system 12 may be described in the general context of computersystem-executable instructions, such as program processes, beingexecuted by a computer system. Generally, program processes may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program processes may belocated in both local and remote computer system storage media includingmemory storage devices.

As shown in FIG. 11 , computer system 12 in computing node 10 is shownin the form of a computing device. The components of computer system 12may include, but are not limited to, one or more processor 16, a systemmemory 28, and a bus 18 that couples various system components includingsystem memory 28 to processor 16. In one embodiment, computing node 10is a computing node of a non-cloud computing environment. In oneembodiment, computing node 10 is a computing node of a cloud computingenvironment as set forth herein in connection with FIGS. 12 and 13 .

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system 12 typically includes a variety of computer systemreadable media. Such media may be any available media that is accessibleby computer system 12, and it includes both volatile and non-volatilemedia, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program processes that are configured to carry out thefunctions of embodiments of the invention.

One or more program 40, having a set (at least one) of program processes42, may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram processes, and program data. One or more program 40 includingprogram processes 42 can generally carry out the functions set forthherein. One or more program 40 including program processes 42 can definemachine logic to carry out the functions set forth herein. In oneembodiment, manager system 110 can include one or more computing node 10and can include one or more program 40 for performing functionsdescribed herein.

Computer system 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computer system12; and/or any devices (e.g., network card, modem, etc.) that enablecomputer system 12 to communicate with one or more other computingdevices. Such communication can occur via Input/Output (I/O) interfaces22. Still yet, computer system 12 can communicate with one or morenetworks such as a local area network (LAN), a general wide area network(WAN), and/or a public network (e.g., the Internet) via network adapter20. As depicted, network adapter 20 communicates with the othercomponents of computer system 12 via bus 18. It should be understoodthat although not shown, other hardware and/or software components couldbe used in conjunction with computer system 12. Examples, include, butare not limited to: microcode, device drivers, redundant processingunits, external disk drive arrays, RAID systems, tape drives, and dataarchival storage systems, etc. In addition to or in place of havingexternal devices 14 and display 24, which can be configured to provideuser interface functionality, computing node 10 in one embodiment caninclude display 25 connected to bus 18. In one embodiment, display 25can be configured as a touch screen display and can be configured toprovide user interface functionality, e.g. can facilitate virtualkeyboard functionality and input of total data. Computer system 12 inone embodiment can also include one or more sensor device 27 connectedto bus 18. One or more sensor device 27 can alternatively be connectedthrough I/O interface(s) 22. One or more sensor device 27 can include aGlobal Positioning Sensor (GPS) device in one embodiment and can beconfigured to provide a location of computing node 10. In oneembodiment, one or more sensor device 27 can alternatively or inaddition include, e.g., one or more of a camera, a gyroscope, atemperature sensor, a humidity sensor, a pulse sensor, a blood pressure(bp) sensor or an audio input device. Computer system 12 can include oneor more network adapter 20. In FIG. 12 computing node 10 is described asbeing implemented in a cloud computing environment and accordingly isreferred to as a cloud computing node in the context of FIG. 12 .

Referring now to FIG. 12 , illustrative cloud computing environment 50is depicted. As shown, cloud computing environment 50 comprises one ormore cloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 12 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 13 , a set of functional abstraction layersprovided by cloud computing environment 50 (FIG. 12 ) is shown. Itshould be understood in advance that the components, layers, andfunctions shown in FIG. 13 are intended to be illustrative only andembodiments of the invention are not limited thereto. As depicted, thefollowing layers and corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and processing components 96 for establishingand updating geofence locations as set forth herein. The processingcomponents 96 can be implemented with use of one or more program 40described in FIG. 11 .

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowcharts and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used herein, thesingular forms “a,” “an,” and “the” are intended to include the pluralforms as well, unless the context clearly indicates otherwise. It willbe further understood that the terms “comprise” (and any form ofcomprise, such as “comprises” and “comprising”), “have” (and any form ofhave, such as “has” and “having”), “include” (and any form of include,such as “includes” and “including”), and “contain” (and any form ofcontain, such as “contains” and “containing”) are open-ended linkingverbs. As a result, a method or device that “comprises,” “has,”“includes,” or “contains” one or more steps or elements possesses thoseone or more steps or elements, but is not limited to possessing onlythose one or more steps or elements. Likewise, a step of a method or anelement of a device that “comprises,” “has,” “includes,” or “contains”one or more features possesses those one or more features, but is notlimited to possessing only those one or more features. Forms of the term“based on” herein encompass relationships where an element is partiallybased on as well as relationships where an element is entirely based on.Methods, products and systems described as having a certain number ofelements can be practiced with less than or greater than the certainnumber of elements. Furthermore, a device or structure that isconfigured in a certain way is configured in at least that way, but mayalso be configured in ways that are not listed.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below, if any, areintended to include any structure, material, or act for performing thefunction in combination with other claimed elements as specificallyclaimed. The description set forth herein has been presented forpurposes of illustration and description, but is not intended to beexhaustive or limited to the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the disclosure. Theembodiment was chosen and described in order to best explain theprinciples of one or more aspects set forth herein and the practicalapplication, and to enable others of ordinary skill in the art tounderstand one or more aspects as described herein for variousembodiments with various modifications as are suited to the particularuse contemplated.

What is claimed is:
 1. A computer-implemented method, comprising:receiving, by a data processing system, digital teaching content and oneor more content heuristics, and wherein the digital teaching contentcomprises a plurality of content items; monitoring, by the dataprocessing system, user gaze of the digital teaching content in by aplurality of users, wherein the monitoring comprises: detecting one ormore reflections of light from at least one eye; and cognitivelyskipping, by the data processing system, at least one of the pluralityof content items of the digital teaching content based on the monitoringand the one or more content heuristics.
 2. The computer-implementedmethod of claim 1, further comprising cognitively modifying, by the dataprocessing system, the digital teaching content based, in part, on themonitoring and the one or more content heuristics.
 3. Thecomputer-implemented method of claim 1, further comprising cognitivelymodifying, by the data processing system, the digital teaching contentbased, in part, on the monitoring and the one or more contentheuristics, wherein the receiving further comprises receiving, by thedata processing system, a hypergraph of the digital teaching content,wherein the cognitively skipping is further based on the hypergraph, andwherein the cognitively modifying is further based on a heat map builtusing the hypergraph and the user gaze.
 4. The computer-implementedmethod of claim 1, further comprising cognitively modifying, by the dataprocessing system, the digital teaching content based, in part, on themonitoring and the one or more content heuristics, wherein thecognitively modifying comprises at least one of generating new digitalteaching content, removing at least one and less than all of theplurality of content items and reordering at least one of the pluralityof content items.
 5. The computer-implemented method of claim 1, furthercomprising cognitively modifying, by the data processing system, thedigital teaching content based, in part, on the monitoring and the oneor more content heuristics, wherein the cognitively modifying comprisesat least one of generating new digital teaching content, removing atleast one and less than all of the plurality of content items andreordering at least one of the plurality of content items, wherein thecognitively modifying comprises determining, by the data processingsystem, a clarity parameter for one or more of the plurality of contentitems using, at least in part, the one or more content heuristics. 6.The computer-implemented method of claim 1, further comprisingcognitively modifying, by the data processing system, the digitalteaching content based, in part, on the monitoring and the one or morecontent heuristics, and the computer-implemented method furthercomprising building, by the data processing system, one or more heatmaps from a sequence of gaze points of the user gaze, wherein theplurality of content items are grouped into a plurality of units,wherein a predetermined unit of the plurality of units is divided into aplurality of zones, wherein the monitoring comprises tracking gaze timeof the plurality of users on each of the plurality of zones, and whereinthe one or more heat maps reflects the gaze time.
 7. Thecomputer-implemented method of claim 1, further comprising cognitivelymodifying, by the data processing system, the digital teaching contentbased, in part, on the monitoring and the one or more contentheuristics, and the computer-implemented method further comprisingbuilding, by the data processing system, one or more heat maps from asequence of gaze points of the user gaze, wherein the plurality ofcontent items are grouped into a plurality of units, wherein apredetermined unit of the plurality of units is divided into a pluralityof zones, wherein the monitoring comprises tracking gaze time of theplurality of users on each of the plurality of zones, and wherein theone or more heat maps reflects the gaze time, the computer-implementedmethod further comprising, prior to the cognitively skipping and thecognitively modifying, ordering, by the data processing system, thedigital teaching content in descending order of attention based on theone or more heat maps.
 8. The computer-implemented method of claim 1,further comprising cognitively modifying, by the data processing system,the digital teaching content based, in part, on the monitoring and theone or more content heuristics, further comprising using machinelearning for the data processing system to improve at least one of thecognitively skipping and the cognitively modifying.
 9. Thecomputer-implemented method of claim 1, wherein the monitoring comprisesgathering, by the data processing system, a sequence of gaze points, thecomputer-implemented method further comprising building, by the dataprocessing system, one or more heat maps from a sequence of gaze points.10. The computer-implemented method of claim 1, wherein the monitoringcomprises gathering, by the data processing system, a sequence of gazepoints, the computer-implemented method further comprising building, bythe data processing system, one or more heat maps from a sequence ofgaze points, wherein the monitoring comprises gathering, by the dataprocessing system, a sequence of gaze points, the computer-implementedmethod further comprising building, by the data processing system, oneor more heat maps from a sequence of gaze points, wherein the gatheringcomprises using, by the data processing system, at least one camera toobserve one or more of the plurality of users and obtain the sequence ofgaze points.
 11. The computer-implemented method of claim 1, wherein themonitoring comprises gathering, by the data processing system, asequence of gaze points.
 12. A system, comprising: a memory; and atleast one processor in communication with the memory, the memory storingprogram code to perform operations, comprising: receiving, by a dataprocessing system, digital teaching content, wherein the digitalteaching content comprises a plurality of content items; monitoring, bythe data processing system, user gaze of the digital teaching content inby a plurality of users, wherein the monitoring comprises: detecting oneor more reflections of light from at least one eye; and cognitivelyskipping, by the data processing system, at least one of the pluralityof content items of the digital teaching content based on themonitoring.
 13. The system of claim 12, further comprising cognitivelymodifying, by the data processing system, the digital teaching contentbased, in part, on the monitoring and one or more content heuristics,wherein the receiving further comprises receiving, by the dataprocessing system, a hypergraph of the digital teaching content, whereinthe cognitively skipping is further based on a heat map built using thehypergraph and the user gaze, and wherein the cognitively modifying isfurther based on the hypergraph and the user gaze.
 14. The system ofclaim 12, further comprising cognitively modifying, by the dataprocessing system, the digital teaching content based, in part, on themonitoring and one or more content heuristics, wherein the receivingfurther comprises receiving, by the data processing system, a hypergraphof the digital teaching content, wherein the cognitively skipping isfurther based on a heat map built using the hypergraph and the usergaze, and wherein the cognitively modifying is further based on thehypergraph and the user gaze, operations further comprising building, bythe data processing system, the heat map from a sequence of gaze pointsof the user gaze, wherein the monitoring comprises tracking gaze time ofthe plurality of users on each of a plurality of zones, and wherein theheat map reflects the gaze time.
 15. The system of claim 12, furthercomprising cognitively modifying, by the data processing system, thedigital teaching content, in part, on the monitoring and one or morecontent heuristics, wherein the cognitively monitoring comprisesgathering, by the data processing system, a sequence of gaze points, theoperations further comprising building, by the data processing system,one or more heat maps from the sequence of gaze points.
 16. The systemof claim 12, further comprising: cognitively modifying, by the dataprocessing system, the digital teaching content based, in part, on themonitoring and one or more content heuristics; and machine learning forthe data processing system to improve at least one of the cognitivelyskipping and the cognitively modifying.
 17. The system of claim 12,wherein the monitoring comprises gathering, by the data processingsystem, a sequence of gaze points.
 18. The system of claim 12, whereinthe monitoring comprises gathering, by the data processing system, asequence of gaze points, the operations further comprising building, bythe data processing system, one or more heat maps from the sequence ofgaze points.
 19. A computer program product, comprising: a computerreadable storage medium readable by a processor and storing instructionsfor performing operations, comprising: receiving, by a data processingsystem, digital teaching content and one or more content heuristics, andwherein the digital teaching content comprises a plurality of contentitems; monitoring, by the data processing system, user gaze of thedigital teaching content by a plurality of users; and cognitivelyskipping, by the data processing system, at least one of the pluralityof content items of the digital teaching content based on the monitoringand the one or more content heuristics.
 20. The computer program productof claim 19, further comprising cognitively modifying, by the dataprocessing system, the digital teaching content based, in part, on themonitoring and the one or more content heuristics, wherein the receivingfurther comprises receiving, by the data processing system, a hypergraphof the digital teaching content, wherein the cognitively skipping isfurther based on the hypergraph, and wherein the cognitively modifyingis further based on a heat map built using the hypergraph and the usergaze.
 21. The computer program product of claim 19, further comprisingcognitively modifying, by the data processing system, the digitalteaching content based, in part, on the monitoring and the one or morecontent heuristics, wherein the receiving further comprises receiving,by the data processing system, a hypergraph of the digital teachingcontent, wherein the cognitively skipping is further based on thehypergraph, and wherein the cognitively modifying is further based on aheat map built using the hypergraph and the user gaze, furthercomprising building, by the data processing system, the heat map from asequence of gaze points of the user gaze, wherein the plurality ofcontent items are grouped into a plurality of units, wherein apredetermined unit of the plurality of units is divided into a pluralityof zones, wherein the monitoring comprises tracking gaze time of theplurality of users on each of the plurality of zones, and wherein theheat map reflects the gaze time.
 22. The computer program product ofclaim 19, further comprising cognitively modifying, by the dataprocessing system, the digital teaching content based, in part, on themonitoring and the one or more content heuristics, wherein themonitoring comprises gathering, by the data processing system, asequence of gaze points, the operations further comprising building, bythe data processing system, one or more heat maps from the sequence ofgaze points.
 23. The computer program product of claim 19, furthercomprising: cognitively modifying, by the data processing system, thedigital teaching content based, in part, on the monitoring and the oneor more content heuristics; and using machine learning for the dataprocessing system to improve at least one of the cognitively skippingand the cognitively modifying.
 24. The computer program product of claim19, wherein the monitoring comprises gathering, by the data processingsystem, a sequence of gaze points.
 25. The computer program product ofclaim 19, wherein the monitoring comprises gathering, by the dataprocessing system, a sequence of gaze points, the operations furthercomprising building, by the data processing system, one or more heatmaps from the sequence of gaze points.